In this paper, two training approaches are used, namely inclusive training and sibling training. In the inclusive training method, all the non-kelp samples from the entire dataset are treated as negative samples i.e., nodes 1.2 and 1.1.2 in Figure REF . However in the sibling training method, only those non-kelp samples are considered as negative which comes under the macroalgae node i.e., node 1.1.2 in Figure REF . We use a linear Support Vector Machines (SVM) [1]} classifier because it has shown excellent performance with features extracted from deep networks [2]}. We use the SVM classifier in a one-vs-all configuration with a linear kernel. We perform 3-fold cross validation within the training set to optimize the SVM parameters and mean performances are reported in Section .
